Thesis (M.S.) University of Alaska Fairbanks, 2010"The objective of this research was to explore a cost-effective and non-invasive methodology to characterize spatial variability of hydraulic conductivity using airborne electromagnetic (AEM) signatures as an alternative to traditional techniques such as borehole sampling. The relationship of AEM measured apparent resistivity and magnetic field strength was explored using a small dataset that included 180 natural moisture (NM) content data and a total dataset of 546 grain size distributions that excluded the NM. The grain size distributions were used to develop soil indicator parameter and to estimate the hydraulic conductivity (K*) using pedo-transfer functions. Predictive models were developed using three techniques; artificial neural network regression (ANNR), support vector regression (SVR), and artificial neural network classification (ANNC). The sole use of non-invasive parameters to characterize K* proved insufficient. The inclusion of supplemental invasively collected parameters showed ANNR to best characterize the relationship (R² = 0.64) with the smaller dataset; while the SVR model performed best with the total dataset (R² = 0.57). ANNC was shown to be a viable alternative (overall accuracy = 88%) when broad characterization of K* was sufficient. This study lays out a methodology that could be used for future K* characterization using improved data set"--Leaf iii.Clarence Berry Fellowship, University of Alaska Fairbanks Graduate School graduate school fellowshi
